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2003.07631
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Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
17 March 2020
Wojciech Samek
G. Montavon
Sebastian Lapuschkin
Christopher J. Anders
K. Müller
XAI
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Papers citing
"Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications"
50 / 122 papers shown
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Artificial intelligence to advance Earth observation: a perspective
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Konrad Schindler
Begüm Demir
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Xiao Xiang Zhu
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Mihai Datcu
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Bertrand Le Saux
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Understanding and Unifying Fourteen Attribution Methods with Taylor Interactions
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Mengnan Du
Weifu Chen
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Ziwei Yang
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Extractive Text Summarization Using Generalized Additive Models with Interactions for Sentence Selection
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Vinícius Camargo Da Silva
João Paulo Papa
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21 Dec 2022
Explainable Analysis of Deep Learning Methods for SAR Image Classification
IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2022
Sheng Su
Ziteng Cui
Weiwei Guo
Zenghui Zhang
Wenxian Yu
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152
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14 Apr 2022
Feature Visualization within an Automated Design Assessment leveraging Explainable Artificial Intelligence Methods
Procedia CIRP (PC), 2022
Raoul Schönhof
Artem Werner
J. Elstner
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Ramez Awad
Marco F. Huber
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149
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28 Jan 2022
Negative Evidence Matters in Interpretable Histology Image Classification
International Conference on Medical Imaging with Deep Learning (MIDL), 2022
Soufiane Belharbi
M. Pedersoli
Ismail Ben Ayed
Luke McCaffrey
Mohammadhadi Shateri
465
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07 Jan 2022
Deep Learning and Earth Observation to Support the Sustainable Development Goals
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Jan Dirk Wegner
Ronny Hansch
D. Tuia
Pedram Ghamisi
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Gustau Camps-Valls
335
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21 Dec 2021
Evaluation of Interpretability for Deep Learning algorithms in EEG Emotion Recognition: A case study in Autism
J. M. M. Torres
Sara E. Medina-DeVilliers
T. Clarkson
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Giuseppe Riccardi
483
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25 Nov 2021
KML: Using Machine Learning to Improve Storage Systems
I. Akgun
A. S. Aydin
Andrew Burford
Michael McNeill
Michael Arkhangelskiy
Aadil Shaikh
L. Velikov
E. Zadok
313
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22 Nov 2021
TorchEsegeta: Framework for Interpretability and Explainability of Image-based Deep Learning Models
S. Chatterjee
Arnab Das
Chirag Mandal
Budhaditya Mukhopadhyay
Manish Vipinraj
Aniruddh Shukla
R. Rao
Chompunuch Sarasaen
Oliver Speck
A. Nürnberger
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268
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16 Oct 2021
Logic Explained Networks
International Workshop on Neural-Symbolic Learning and Reasoning (NeSy), 2021
Gabriele Ciravegna
Pietro Barbiero
Francesco Giannini
Marco Gori
Pietro Lio
Marco Maggini
S. Melacci
318
95
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11 Aug 2021
Entropy-based Logic Explanations of Neural Networks
AAAI Conference on Artificial Intelligence (AAAI), 2021
Pietro Barbiero
Gabriele Ciravegna
Francesco Giannini
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Marco Gori
S. Melacci
FAtt
XAI
502
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12 Jun 2021
Understanding Neural Code Intelligence Through Program Simplification
Md Rafiqul Islam Rabin
Vincent J. Hellendoorn
Mohammad Amin Alipour
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317
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07 Jun 2021
A General Taylor Framework for Unifying and Revisiting Attribution Methods
Huiqi Deng
Na Zou
Mengnan Du
Weifu Chen
Guo-Can Feng
Helen Zhou
TDI
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254
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A Comprehensive Taxonomy for Explainable Artificial Intelligence: A Systematic Survey of Surveys on Methods and Concepts
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Gesina Schwalbe
Bettina Finzel
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299
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Mutual Information Preserving Back-propagation: Learn to Invert for Faithful Attribution
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Huiqi Deng
Na Zou
Weifu Chen
Guo-Can Feng
Mengnan Du
Helen Zhou
FAtt
235
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14 Apr 2021
Towards a Collective Agenda on AI for Earth Science Data Analysis
IEEE Geoscience and Remote Sensing Magazine (GRSM), 2021
D. Tuia
R. Roscher
Jan Dirk Wegner
Nathan Jacobs
Xiaoxiang Zhu
Gustau Camps-Valls
AI4CE
311
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11 Apr 2021
Evaluating explainable artificial intelligence methods for multi-label deep learning classification tasks in remote sensing
International Journal of Applied Earth Observation and Geoinformation (JAEOG), 2021
Ioannis Kakogeorgiou
Konstantinos Karantzalos
XAI
242
150
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03 Apr 2021
Towards a mathematical framework to inform Neural Network modelling via Polynomial Regression
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Pablo Morala
Jenny Alexandra Cifuentes
R. Lillo
Iñaki Ucar
265
41
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07 Feb 2021
Towards Robust Explanations for Deep Neural Networks
Pattern Recognition (Pattern Recognit.), 2020
Ann-Kathrin Dombrowski
Christopher J. Anders
K. Müller
Pan Kessel
FAtt
349
67
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18 Dec 2020
Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images via Max-Min Uncertainty
IEEE Transactions on Medical Imaging (TMI), 2020
Soufiane Belharbi
Jérôme Rony
Jose Dolz
Ismail Ben Ayed
Luke McCaffrey
Mohammadhadi Shateri
332
60
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14 Nov 2020
Exemplary Natural Images Explain CNN Activations Better than State-of-the-Art Feature Visualization
Judy Borowski
Roland S. Zimmermann
Judith Schepers
Robert Geirhos
Thomas S. A. Wallis
Matthias Bethge
Wieland Brendel
FAtt
317
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23 Oct 2020
Geometric Disentanglement by Random Convex Polytopes
M. Joswig
M. Kaluba
Lukas Ruff
205
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29 Sep 2020
A Unifying Review of Deep and Shallow Anomaly Detection
Proceedings of the IEEE (Proc. IEEE), 2020
Lukas Ruff
Jacob R. Kauffmann
Robert A. Vandermeulen
G. Montavon
Wojciech Samek
Matthias Kirchler
Thomas G. Dietterich
Klaus-Robert Muller
UQCV
765
984
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24 Sep 2020
MeLIME: Meaningful Local Explanation for Machine Learning Models
T. Botari
Frederik Hvilshoj
Rafael Izbicki
A. Carvalho
AAML
FAtt
335
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12 Sep 2020
Langevin Cooling for Domain Translation
Vignesh Srinivasan
Klaus-Robert Muller
Wojciech Samek
Shinichi Nakajima
271
1
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31 Aug 2020
A Unified Taylor Framework for Revisiting Attribution Methods
Huiqi Deng
Na Zou
Mengnan Du
Weifu Chen
Guo-Can Feng
Helen Zhou
FAtt
TDI
438
24
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21 Aug 2020
Explainable Deep One-Class Classification
Philipp Liznerski
Lukas Ruff
Robert A. Vandermeulen
Billy Joe Franks
Matthias Kirchler
Klaus-Robert Muller
545
238
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03 Jul 2020
How Much Can I Trust You? -- Quantifying Uncertainties in Explaining Neural Networks
Kirill Bykov
Marina M.-C. Höhne
Klaus-Robert Muller
Shinichi Nakajima
Matthias Kirchler
UQCV
FAtt
423
34
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16 Jun 2020
Rethinking Assumptions in Deep Anomaly Detection
Lukas Ruff
Robert A. Vandermeulen
Billy Joe Franks
Klaus-Robert Muller
Matthias Kirchler
548
96
0
30 May 2020
Finding and Removing Clever Hans: Using Explanation Methods to Debug and Improve Deep Models
Christopher J. Anders
Talmaj Marinc
David Neumann
Wojciech Samek
K. Müller
Sebastian Lapuschkin
AAML
299
20
0
22 Dec 2019
On the Explanation of Machine Learning Predictions in Clinical Gait Analysis
D. Slijepcevic
Fabian Horst
Sebastian Lapuschkin
Anna-Maria Raberger
Matthias Zeppelzauer
Wojciech Samek
C. Breiteneder
W. Schöllhorn
B. Horsak
292
78
0
16 Dec 2019
Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability
Journal of Advances in Modeling Earth Systems (JAMES), 2019
B. Toms
E. Barnes
I. Ebert‐Uphoff
AI4CE
307
248
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04 Dec 2019
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI
Information Fusion (Inf. Fusion), 2019
Alejandro Barredo Arrieta
Natalia Díaz Rodríguez
Javier Del Ser
Adrien Bennetot
Siham Tabik
...
S. Gil-Lopez
Daniel Molina
Richard Benjamins
Raja Chatila
Francisco Herrera
XAI
1.2K
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22 Oct 2019
Towards Best Practice in Explaining Neural Network Decisions with LRP
IEEE International Joint Conference on Neural Network (IJCNN), 2019
M. Kohlbrenner
Alexander Bauer
Shinichi Nakajima
Alexander Binder
Wojciech Samek
Sebastian Lapuschkin
482
170
0
22 Oct 2019
Contextual Prediction Difference Analysis for Explaining Individual Image Classifications
Jindong Gu
Volker Tresp
FAtt
171
8
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21 Oct 2019
Understanding Deep Networks via Extremal Perturbations and Smooth Masks
IEEE International Conference on Computer Vision (ICCV), 2019
Ruth C. Fong
Mandela Patrick
Andrea Vedaldi
AAML
342
475
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18 Oct 2019
Explaining image classifiers by removing input features using generative models
Chirag Agarwal
Anh Totti Nguyen
FAtt
539
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09 Oct 2019
Explaining and Interpreting LSTMs
L. Arras
Jose A. Arjona-Medina
Michael Widrich
G. Montavon
Michael Gillhofer
K. Müller
Sepp Hochreiter
Wojciech Samek
FAtt
AI4TS
290
85
0
25 Sep 2019
Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey
Machine Learning for Biomedical Imaging (MLBI), 2019
Jérôme Rony
Soufiane Belharbi
Jose Dolz
Ismail Ben Ayed
Luke McCaffrey
Eric Granger
517
76
0
08 Sep 2019
Deep neural network or dermatologist?
Kyle Young
Gareth Booth
B. Simpson
R. Dutton
Sally Shrapnel
MedIm
221
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19 Aug 2019
Resolving challenges in deep learning-based analyses of histopathological images using explanation methods
Scientific Reports (Sci Rep), 2019
Miriam Hagele
P. Seegerer
Sebastian Lapuschkin
M. Bockmayr
Wojciech Samek
Frederick Klauschen
K. Müller
Alexander Binder
325
178
0
15 Aug 2019
Explaining Convolutional Neural Networks using Softmax Gradient Layer-wise Relevance Propagation
Brian Kenji Iwana
Ryohei Kuroki
S. Uchida
FAtt
339
110
0
06 Aug 2019
Explanations can be manipulated and geometry is to blame
Neural Information Processing Systems (NeurIPS), 2019
Ann-Kathrin Dombrowski
Maximilian Alber
Christopher J. Anders
M. Ackermann
K. Müller
Pan Kessel
AAML
FAtt
484
379
0
19 Jun 2019
A Rate-Distortion Framework for Explaining Neural Network Decisions
Jan Macdonald
S. Wäldchen
Sascha Hauch
Gitta Kutyniok
182
42
0
27 May 2019
NeuralHydrology -- Interpreting LSTMs in Hydrology
Frederik Kratzert
M. Herrnegger
D. Klotz
Sepp Hochreiter
Günter Klambauer
206
100
0
19 Mar 2019
Interpretable Deep Learning in Drug Discovery
Kristina Preuer
Günter Klambauer
F. Rippmann
Sepp Hochreiter
Thomas Unterthiner
247
100
0
07 Mar 2019
Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
Nature Communications (Nat Commun), 2019
Sebastian Lapuschkin
S. Wäldchen
Alexander Binder
G. Montavon
Wojciech Samek
K. Müller
448
1,158
0
26 Feb 2019
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